Valorisation of smart grid monitoring data

Smart grids monitor connected appliances continuously mostly for accounting. A promising application is the pooling of heat pumps to provide ancillary services. The save operation necessitates the remote detection of the heat pump’s operation state from the power time series. In our contribution, two classification approaches are presented and validated against real-world data: First, a support vector machines algorithm attributes states to individual segments of heat pump activity and achieves accuracies above 98% for real-world data with a training set of only 50 segments. Second, a deep neural network algorithm classifies short sequences within individual
segments of heat pump activity and achieves classification accuracies above 90%.
Eventually, an outlook towards remote diagnosis of a heat pump, future services in energy efficiency consulting and predictive maintenance is provided.